Competing Risks Power Analysis (Specialized) — competingRisksPower • ClinicoPath

Power analysis and sample size calculations for competing risks studies. Calculates power for testing differences in cumulative incidence functions between groups using Gray's test and subdistribution hazard models.

Usage

competingRisksPower(
  data,
  analysisType = "power",
  alpha = 0.05,
  power = 0.8,
  totalSampleSize = 200,
  allocationRatio = "1:1",
  followUpTime = 5,
  accrualTime = 2,
  eventRate1 = 0.3,
  competingRate1 = 0.2,
  eventRate2 = 0.4,
  competingRate2 = 0.2,
  hazardRatio = 1.5,
  testType = "gray",
  distributionType = "exponential",
  shape1 = 1,
  shape2 = 1,
  numberOfSimulations = 1000,
  showSimulationDetails = FALSE,
  showEducational = TRUE,
  plotPowerCurve = TRUE,
  plotEventRates = FALSE,
  sensitivityAnalysis = FALSE,
  confidenceLevel = 0.95
)

Arguments

data: The data as a data frame (optional for power calculations).
analysisType: Type of power analysis to perform
alpha: Type I error rate for statistical testing
power: Desired statistical power (for sample size calculations)
totalSampleSize: Total sample size for power calculation
allocationRatio: Allocation ratio between groups (e.g., "1:1", "2:1")
followUpTime: Maximum follow-up time for the study
accrualTime: Patient accrual/recruitment period
eventRate1: Cumulative incidence rate for primary event in Group 1
competingRate1: Cumulative incidence rate for competing events in Group 1
eventRate2: Cumulative incidence rate for primary event in Group 2
competingRate2: Cumulative incidence rate for competing events in Group 2
hazardRatio: Expected subdistribution hazard ratio between groups
testType: Type of statistical test for competing risks comparison
distributionType: Assumed distribution for event times
shape1: Shape parameter for Weibull distribution (Group 1)
shape2: Shape parameter for Weibull distribution (Group 2)
numberOfSimulations: Number of Monte Carlo simulations for power estimation
showSimulationDetails: Display detailed simulation results and convergence diagnostics
showEducational: Display educational information about competing risks power analysis
plotPowerCurve: Display power curve across different effect sizes or sample sizes
plotEventRates: Display visualization of cumulative incidence rates
sensitivityAnalysis: Perform sensitivity analysis across different parameter values
confidenceLevel: Confidence level for power estimates

Value

A results object containing:

`results$todo`					a html
`results$educationalInfo`					a html
`results$powerResults`					a table
`results$studyDesignTable`					a table
`results$sampleSizeBreakdown`					a table
`results$powerCurveTable`					a table
`results$sensitivityTable`					a table
`results$simulationDiagnostics`					a table
`results$methodsInfo`					a html
`results$recommendationsInfo`					a html
`results$powerCurvePlot`					an image
`results$eventRatesPlot`					an image

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$powerResults$asDF

as.data.frame(results$powerResults)

Details

This is the specialized function for competing risks power analysis. For unified analysis across all survival study types, also consider 'Comprehensive Survival Power Analysis'.

Examples

# \donttest{
# example will be added
# }